Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome – the MADDEC study

References

• Hemingway H, Asselbergs FW, Danesh J, et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J. 2018;39:1481–1495.
   Web of Science ®Google Scholar
• Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131:269–279.
   PubMed Web of Science ®Google Scholar
• Fox KAA, Gore JM, Eagle KA, et al. Rationale and design of the grace (global registry of acute coronary events) project: a multinational registry of patients hospitalized with acute coronary syndromes. Am Heart J. 2001;141:190–199.
   PubMed Web of Science ®Google Scholar
• Granger C, Goldberg R, Dabbous O, et al. Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med. 2003;163:2345–2353.
   PubMed Web of Science ®Google Scholar
• Fox KAA, Dabbous OH, Goldberg RJ, et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ. 2006;333:1091–1091.
   PubMed Web of Science ®Google Scholar
• D’Ascenzo F, Biondi-Zoccai G, Moretti C, et al. TIMI, GRACE and alternative risk scores in Acute Coronary Syndromes: a meta-analysis of 40 derivation studies on 216,552 patients and of 42 validation studies on 31,625 patients. Contemp Clin Trials. 2012;33:507–514.
   PubMed Web of Science ®Google Scholar
• Kim J. Big data, health informatics, and the future of cardiovascular medicine. J Am Coll Cardiol. 2017;69:899–902.
   PubMed Web of Science ®Google Scholar
• Motwani M, Dey D, Berman DS, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017;38:500–507.
   PubMed Web of Science ®Google Scholar
• Loghmanpour NA, Kormos RL, Kanwar MK, et al. A Bayesian model to predict right ventricular failure following left ventricular assist device therapy. JACC Hear Fail. 2016;4:711–721.
   PubMed Web of Science ®Google Scholar
• Weng SF, Reps J, Kai J, et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12:1–15.
   Web of Science ®Google Scholar
• Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Statist. 2001;29:1189–1232.
   Web of Science ®Google Scholar
• Hernesniemi JA, Mahdiani S, Lyytikäinen LP, et al. Cohort description for MADDEC – mass data in detection and prevention of serious adverse events in cardiovascular disease. In: Eskola H, Väisänen O, Viik J, Hyttinen J. editors. EMBEC & NBC 2017. IFMBE Proceedings. Vol. 65. Singapore: Springer.
   Google Scholar
• Roffi M, Patrono C, Collet J-P, et al. 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J. 2015;32:2999–3054.
   Google Scholar
• Ibanez B, James S, Agewall S, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2018;39:119–177.
   PubMed Web of Science ®Google Scholar
• van BS, Groothuis-Oudshoorn K. mice:Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45:1–67.
   Web of Science ®Google Scholar
• Beasley TM, Erickson S, Allison DB. Rank-based inverse normal transformations are increasingly used, but are they merited? Behav Genet. 2009;39:580–595.
   PubMed Web of Science ®Google Scholar
• Venables WN, Ripley BD. Modern applied statistics with S. Springer; 2002.
   Google Scholar
• Kolek MJ, Graves AJ, Xu M, et al. Evaluation of a prediction model for the development of atrial fibrillation in a repository of electronic medical records. JAMA Cardiol. 2016;1:1007–1013.
   PubMed Web of Science ®Google Scholar
• Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines. Circulation. 2014;129:S49–73.
   Google Scholar
• Spertus JV, T. Normand S-L, Wolf R, et al. Assessing hospital performance after percutaneous coronary intervention using big data. Circ Cardiovasc Qual Outcomes. 2016;9:659–669.
   PubMed Web of Science ®Google Scholar
• Miotto R, Li L, Kidd BA, et al. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016;17:26094.
   Web of Science ®Google Scholar
• Shickel B, Tighe PJ, Bihorac A, et al. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018;22:1589–1604.
   Google Scholar
• Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. JAMA. 2014;311:2479–2480.
   PubMed Web of Science ®Google Scholar
• Pajunen P, Koukkunen H, Ketonen M, et al. The validity of the Finnish hospital discharge register and causes of death register data on coronary heart disease. Eur J Prev Cardiol. 2005;12:132–137.
   Google Scholar
• Tolonen H, Salomaa V, Torppa J, et al. The validation of the Finnish hospital discharge register and causes of death register data on stroke diagnoses. Eur J Prev Cardiol. 2007;14:380–385.
   Google Scholar
• Sund R. Quality of the Finnish hospital discharge register: a systematic review. Scand J Public Health. 2012;40:505–515.
   PubMed Web of Science ®Google Scholar
• Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 2002;38:367–378.
   Web of Science ®Google Scholar